Recently I had conducted Azure Machine Learning workshop at Jakarta, in partnership with Algoritma Indonesia. Around 10 attended the workshop. Employees from Telkomsel, Government Universities and few other companies attended the workshop.

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This 3 days training focus on getting started with Data Science technologies. You will learn Azure machine learning studio, R studio, Jupyter Notebook , Spyder with Python for data science. This course includes real world usage of machine learning for regression, classification and product recommendations.

Day 1

Introduction to Machine Learning

This module introduces machine learning and discussed how algorithms and languages are used.

Lessons

· What is machine learning?

· Introduction to machine learning algorithms

· Introduction to machine learning languages

Introduction to Azure Machine Learning

Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.

Lessons

· Azure machine learning overview

· Introduction to Azure machine learning studio

· Developing and hosting Azure machine learning applications

Managing Datasets

At the end of this module the student will be able to explore various types of data in Azure machine learning.

Lessons

· Categorizing your data

· Importing data to Azure machine learning

· Exploring and transforming data in Azure machine learning

Building Azure Machine Learning Models

This module describes how to use regression algorithms and neural networks with Azure machine learning.

Lessons

· Azure machine learning workflows

· Using regression algorithms

· Using neural networks

Using Azure Machine Learning Models

This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.

Lessons

· Deploying and publishing models

· Consuming Experiments

Day 2

Introduction to R

· Using the R console

· Learning about the environment

· Writing and executing scripts

· Object oriented programming

· Installing packages

· Working directory

· Saving your work

Variable types and data structures

· Variables and assignment

· Data types

· Numeric, character, boolean, and factors

· Data structures

· Vectors, matrices, arrays,

· Assigning new values

· Viewing data and summaries

Base graphics system in R

· Scatterplots, histograms, barcharts, box and whiskers, dotplots

· Labels, legends, titles, axes

· Exporting graphics to different formats

General linear regression

· Linear and logistic models

· Regression plots

· Interaction in regression

Day 3

Introduction to Python

· Python History

· Users of Python

· Installing Python

· Installing IDE

Datatypes

· Numbers

· Sequences

· File

· Tuples

· Dictionaries

Data Science Intro

· Why Python for Data Science

· Popular packages

· Use cases

· Popular Libraries

· Panda

· Numpy

· Matplotlib

· Scikit-learn

Working with data

· Reading & Writing to different data sources

· Cleaning data

· Visualisation

· Data Transformation

Conducted by-

Uditha Bandara (MVP) is specializes in Microsoft development , AI, Mobile App, Cloud and Software Testing technologies. He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional). He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Indonesia, Sri Lanka and India. He has published several books, articles, tutorials, and demos on his Blog – https://uditha.wordpress.com